Image Classification of Tomato Leaf Diseases using Convolutional Neural Network
Article Sidebar
Main Article Content
Abstract
Tomato is one of the most important cultivated vegetable plants in the world. The continuous expanded production and consumption of tomato has grown quickly. It is considered a mainstay of many economic country. Tomato crops can be damage due to various kinds of diseases that are recently discovery of diagnosis errors or not prevented and controlled timely. The problems faced by farmers are typically unnoticed and lack knowledge in crop production. For developing an early treatment process, the identify infections of plant diseases in a rapid can help to reduce huge economical suffering. In agricultural practices is detect of disease manually on crops which is very complex, time-consuming and more tedious tasks. This paper discussing the technique base on digital image processing, which employs the convolutional neural network deep learning model to classify tomato leaf diseases. The dataset is classified into 10 classes: bacterial leaf spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, cucumber mosaic virus, yellow leaf curl virus and fresh leaf. The approach is composed of four main phases: (1) data preprocessing, (2) generated model convolutional neural network, and (3) model evaluation and (4) deployment. The experimental results indicated that deep learning with convolutional neural network technique has the highest effectiveness in recognizing tomato leaf diseases with the total average accuracy at 87.96% at learning rate 0.001 for 100 epochs.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
Grieneisen, M.L., Aegerter, B.J., Scott Stoddard, C., Zhang, M., “Yield and fruit quality of grafted tomatoes, and their potential for soil fumigant use reduction,” A meta-analysis. Agronomy for Sustainable Development, vol. 38, no. 29, 2018.
Hasan, R.I., Yusuf, S.M., Alzubaidi, L., “Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion,” Plants, vol. 9, no. 10,pp. 1–25, 2020.
Thummabenjapone, P. & Phola, S., “A highly potential fungicide to control Stemphylium sp., a causal agent of gray spot of tomato,” In Proceeding The 8th National Plant Protection Conference. pp. 383-391, 2007.
Dookie, M., Ali, O., Ramsubhag, A., and Jayaraman, J., “Flowering gene regulation in tomato plants treated with brown seaweed extracts,” Scientia Horticulturae. vol. 276, 2021.
Durmus, H., Gunes, E. O., and Kirci, M., “Disease detection on the leaves of the tomato plants by using deep learning”, 2017 6th international conference on agro-geoinformatics. Agro-Geoinformatics, pp. 1–5, 2017.
Mortazi, A.; Bagci, U., “Automatically designing CNN architectures for medical image segmentation,” In Proceedings of the International Workshop on Machine Learning in Medical Imaging, Granada, Spain, pp. 98–106, 2018.
Prabira Kumar Sethya, Nalini Kanta Barpandaa, Amiya Kumar Rathb, Santi Kumari Beherab, “Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey,” Procedia Computer Science, 167, pp. 516–530, 2020.
Mohanty SP, Hughes DP, Salath M., “Using deep learning for image-based plant disease detection,” Front Plant Sci, vol. 7. no. 1419. 2016.
The Plant Village dataset:https:// www.kaggle.com/ emmarex / plantdisease
Amara J, Bouaziz B, Algergawy A., “A deep learning based approach for banana leaf diseases classification,” International Journal of Creative Research Thoughts, vol 6. Issue 2, pp. 79-88. 2017.
Rangarajan, A.K.; Purushothaman, R.; Ramesh, A. Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 2018, 133, 1040–1047.
Sangeetha, R.; Rani, M., “Tomato Leaf Disease Prediction Using Transfer Learning,” In Proceedings of the International Advanced Computing Conference 2020, Panaji, India, 2020.
Too, E.C.; Yujian, L.; Njuki, S.; Yingchun, L., “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, 2019.
Agarwal, M.; Gupta, S.K.; Biswas, K.K., “Development of Efficient CNN model for Tomato crop disease identification. Sustain,” Comput. Inform. Syst. vol. 28, pp. 100407–100421, 2020.
M. A. Tanner, and W. H. Wong, “The Calculation of Posterior Distributions By Data Augmentation,” Journal Of The American Statistical Association, vol. 82 no. 398, pp.528–540, 1987.
M. D. Bloice, C. Stocker, and A. Holzinger, “Augmentor: An Image Augmentation Library for Machine Learning,” The Journal of Open Source Software, vol.2. pp. 1-5, 2017.